Cleo
Machine Learning Engineering Manager - Growth
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Machine Learning Engineering Manager - Growth
About The Company
About Cleo
At Cleo, we're not just building another fintech app. We're embarking on a mission to fundamentally change humanity's relationship with money. Imagine a world where everyone, regardless of background or income, has access to a hyper-intelligent financial advisor in their pocket. That's the future we're creating.
Cleo is a rare success story: a profitable, fast-growing unicorn with over $300 million in ARR and growing over 2x year-over-year. This isn't just a job; it's a chance to join a team of brilliant, driven individuals who are passionate about making a real difference. We have an exceptionally high bar for talent, seeking individuals who are not only at the top of their field but also embody our culture of collaboration and positive impact.
If you’re a leader driven by complex challenges, the potential to shape transformative systems, and growth aligned with a fast-scaling company, we invite you to join us.
Follow us on LinkedIn to keep up to date with new product features and insights from the team.
About The Role
We're seeking an exceptional ML Engineering Manager to lead the Machine Learning efforts across our Growth team, a squad responsible for optimising smart, personalised decisions for 4M+ users. This includes deciding what users see, when they see it, and how we maximise long-term value.
Key Accountabilities
- Lead an ML roadmap prioritising high-impact projects that connect product systems with business growth metrics
- Work closely with Marketing Engineers, Product Designers, Product Managers, and Data Scientists to build systems that drive revenue while protecting long-term value
- Direct 3-5 ML Engineers (including individual contributors and emerging managers) with a focus on career growth, high impact, and pragmatic shipping schedules
What We're Building
The Growth team spans two squads:
- Growth Marketing: Acquisition, channel management, phased campaign launches
- Growth Personalisation: Real-time on-app prompts, offers, and recommendations
Key ML systems include:
- Recommender systems that decide which offer/action to present to each user
- Personalised messaging engines tailored to user context and interaction history
- Incrementality testing to quantify causal impact from interventions
- Multi-arced bandit experiments for real-time balancing of revenue and retention
- Automated attribution, scoring, and ranking systems safeguarding short-term revenue while optimising long-term relationship value
What You’ll Be Doing
1. ML Strategy & Delivery
- Collaborate with Product Managers and leadership to define ML opportunities at scale
- Champion test-driven ML development—ensuring our systems focus on measurable impact, not just accuracy or engagement
- Accountable for end-to-end model delivery:
- Defining clear problem propositions
- Performing incremental prototype testing
- Orchestrating deployment at production scale
2. Build and Mentor Your Team
- Technical leadership: Recruit, onboard, and mentor a diverse mix of senior ML engineers and promising talent
- Encourage collaboration and best practices while maintaining urgency—prioritising both quality and delivery velocity
- Own engineering culture: Foster an environment where problems are solved through pre-ship reviews and iterative improvement
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3. Collaborate at Scale
- Synchronise with Growth Marketing Engineering on experimentation, deployment pipelines, and infrastructure decisions
- Work with Product designers to define and refine prompts, interfaces, and messaging flows
- Partner with Analytics teams to validate metrics, instrument experiments, and propagate causal insights internally and externally
4. Technical Ownership
- Code reviews and architectural oversight on all ML build outs, ensuring practical, reliable systems able to handle scale
- Evaluate scalability trade-offs, latency requirements, and model drifts for each application case
- Strengthen teamwork around shared codebases, ML infrastructure, and observed production ML quality metrics
5. Default to Experimenting
- Agent for rigorous causal designs—holdout groups, incrementality checks, uncertainty quantification
- Publish insights: Share learning via internal workshops and external research fora to build Cleo’s ML knowledge foundation
- Foster diagnostic culture—treat model failures as learning opportunities, not “noise”
About You
Qualifications
1. Technical Leadership
- 5+ years in ML/Data Science, including 2+ years in senior individual contributor or leadership roles (where you’ve advised peers on model design, architecture, and production challenges)
- Hands-on experience shipping hybrid online/offline systems that blend statistical rigor with commercial logic
- Strong fluency in ML fundamentals: ranking decisions, causal inference, bandwidth constraints (latency/pipelines), production monitoring
- Cloud-native know-how, with Python as a primary language (cython/net not required but useful)
2. Domain Experiences & Shipments
- Three+ shipping credits in one or more:
- Recommender/Uplift/Scoring systems for personalisation, ads, auctions, feeds (e.g. Spotify/Wallet.fi, newsfeeds, R&D dashboards)
- Incrementality studies for growth marketing payloads (attribution, channel modelling, demand parks)
- Multi-armed bandit platforms or channel optimisation experiments (AOI broadcast, auction micro-strategies)
- Genuine interest in Growth Marketing toolchains—an intuitive understanding of CAC/LTV, retention/friction reduction, and causality with large-scale systems
3. Team Scaling Experience
- Performance mentorship: Developing individual contributors from early-career Talent to contributors who mentor
- Bilateral coaching: Offering pure strategy direction (prioritised goals) while allowing personal autonomy (MD in progress)
- Empathy for shipping: Directing agile delivery but codifying quality signals (not just success/failure criteria, but also developer onboarding processes)
4. Open Community Involvement
- Prior open-sourcing or professional publishing on ML topics (papers, research forums, engineering blogs)
- Culture of knowledge sharing—whether through talks, mentoring junior hires, or curriculum development
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What Makes You a Good Fit
A candidate attractive to us will exhibits:
- Playbooks: Designs experiments with clearly articulated success/failure criteria for experimentation
- Domain-backed models: Models ship with explicit constraints (e.g. “for low-revenue customers, we never recommend outside promotions”)
- Dev Meanwhile: Builds investing dev practices (documentation, logging) but minimises operational overhead
- Inclusive feedback loop: Product and product managers engage with your direct data-driven reasoning before set up phases
- Notion of scale: Scaling sytems while keeping pace with growing orgs ( органы, acronyms, nomenclature bbox)
Mindset & Values
We value candidates who:
- Have product-scaler knack—enthusiasm for optimising product features while their team’s direct users feel its impact
- Prioritise alignment w^{qualities above over imaginary hardware/software fetish}
- Embrace respectful criticism—comments that improve, never diminish
- Major transparency (mismanaging dependencies in problemspotting analyses or ambiguous user value)
- Continue to specialize picky based on experience—reviewing peers’ reports even when the problem seems repeatable
Compensation & Benefits
- Compensation: £150k–170k (London hybrid / £140k–160k UK based) including base salary, equity, and termly OKR reviews (Feb, Jun, Nov)
- Equity: Continuously aligned to company growth—fresh rounds review allocations
- Work culture:
- Mission-driven contribute: You should be excited by the chance to build proactive clients alongside
- Flexible: E.g. regular days noticeable spend fest in office
- Growth mind value: Careers are encouraged to flex their stammad holders and well-defined future?
- Travel and tech stipends
Health & Well-being
- Couples’ nursery subsidies
- Paid medical—supported health insurance plans
- 1M Work Friends Incentives (CFI, global and inclusive)
- 1m powered access to void (divided time location)
Professional Development
- 4 month performance reviews per quarter with higher paid rate
- 25 days leave + public holidays (+1 extra for every year)
- **Sabbatical opens by year 4
- Global tools & tacking (e.g. better than self-host)
Team Belonging
- globally distributed + global socials & virtual team building
- welcome voice-throughly—(separate public discount encryption on external)
Recruitment Process
Interviewers interview respectively:
- HR: Initial company alignment discussion (30 mins).
- Hiring Manager (30 mins)—Cleo mission, learning problems, industry-givings discussion.
- ML Coding Test: Python problem-solve ML building engines via A/B test scenarios (45 mins)
- Whiteboard Design: Tackle an applied thesis/logic around trait training zone or rebuttal proof design (60 mins)
- Managerial Chess: Ship recently, coordinate any support request during interview all collaboration (60 mins)
Statement
By submitting this application and confirmation paragraph against.
To citizen “by submitting this application, I confirm that all information drawn by Cleo AI to share personal Org analytical privacy-debriefs conflicts.”
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